+**Bagging**, or Bootstrap Aggregating, is an [Ensemble Learning](/wiki/Ensemble_Learning) technique in [Machine Learning](/wiki/Machine_Learning). It builds multiple models from resampled data subsets, then combines their predictions to reduce variance and improve overall accuracy, effectively combatting [Overfitting](/wiki/Overfitting).
+## See also
+- [Ensemble Learning](/wiki/Ensemble_Learning)
+- [Random Forest](/wiki/Random_Forest)
+- [Boosting](/wiki/Boosting)
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